{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>15691863</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>15706071</td>\n",
       "      <td>Male</td>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>15654296</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>15755018</td>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>15594041</td>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0    15624510    Male   19            19000          0\n",
       "1    15810944    Male   35            20000          0\n",
       "2    15668575  Female   26            43000          0\n",
       "3    15603246  Female   27            57000          0\n",
       "4    15804002    Male   19            76000          0\n",
       "..        ...     ...  ...              ...        ...\n",
       "395  15691863  Female   46            41000          1\n",
       "396  15706071    Male   51            23000          1\n",
       "397  15654296  Female   50            20000          1\n",
       "398  15755018    Male   36            33000          0\n",
       "399  15594041  Female   49            36000          1\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(r'C:\\Users\\碌卡\\Desktop\\python编程学习\\python数据分析\\100daysML_file\\100day\\datasets\\Social_Network_Ads.csv')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:,[2,3]].values\n",
    "y = dataset.iloc[:,4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(criterion='entropy',random_state=0)\n",
    "classifier = classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0\n",
      " 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 1 1 1 0 0\n",
      " 0 0 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 1 1 1]\n",
      "0.9\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "score = classifier.score(X_test,y_test)\n",
    "print(y_pred)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[61  7]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set,Y_set = X_train,y_train\n",
    "X1,X2 = np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.01)\n",
    "                   ,np.arange(start=X_set[:,1].min()-1,stop=X_set[:,1].max()+1,step=0.01))\n",
    "plt.contourf(X1,X2\n",
    "            ,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape)\n",
    "            ,alpha = 0.75\n",
    "            ,cmap = ListedColormap(('red','green')))\n",
    "plt.xlim(X1.min(),X1.max())\n",
    "plt.ylim(X2.min(),X2.max())\n",
    "for i,j in enumerate(np.unique(Y_set)):\n",
    "    plt.scatter(X_set[Y_set==j,0]\n",
    "               ,X_set[Y_set==j,1]\n",
    "               ,c = ListedColormap(('red','green'))(i)\n",
    "               ,label=j)\n",
    "plt.title('LOGISTIC(Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_set,Y_set = X_test,y_test\n",
    "X1,X2 = np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.01)\n",
    "                   ,np.arange(start=X_set[:,1].min()-1,stop=X_set[:,1].max()+1,step=0.01))\n",
    "plt.contourf(X1,X2\n",
    "            ,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape)\n",
    "            ,alpha = 0.75\n",
    "            ,cmap = ListedColormap(('red','green')))\n",
    "plt.xlim(X1.min(),X1.max())\n",
    "plt.ylim(X2.min(),X2.max())\n",
    "for i,j in enumerate(np.unique(Y_set)):\n",
    "    plt.scatter(X_set[Y_set==j,0]\n",
    "               ,X_set[Y_set==j,1]\n",
    "               ,c = ListedColormap(('red','green'))(i)\n",
    "               ,label=j)\n",
    "plt.title('LOGISTIC(Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "34dfee18d5f4a96df9a8fcc719c91cf50e8ed50de2aa108bf45cd20982063274"
  },
  "kernelspec": {
   "display_name": "Python 3.7.0 64-bit ('base': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
